EDA for Pet-Inclusive Housing Project
VCAP
r.Sys.Date()
Loading and Preparing Data
Research Questions
“What socioeconomic factors (race, income, etc.) are predictive of housing being more or less pet inclusive?”
Another specific question that we want to answer is: “How does average rent vary as a function of pet inclusive housing?
Atlanta Market
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Average Rent vs Pet Inclusivity Score by RPL THEME
All Themes
## `geom_smooth()` using formula = 'y ~ x'Socioeconomic Status
## `geom_smooth()` using formula = 'y ~ x'Household Characteristics
## `geom_smooth()` using formula = 'y ~ x'Race & Ethinc Minorities
## `geom_smooth()` using formula = 'y ~ x'Housing Type & Transportation
## `geom_smooth()` using formula = 'y ~ x'Desnsity Plots of PI SCORE by RPL THEMES
All Themes
Socioeconomic Status
Household Characteristics
Race & Ethinc Minorities
Housing Type & Transportation
Bar Charts of True/False Columns
Average Rent Boxplot by PetsAllow
Percent Frequency of RPL THEMES
| RPL Theme | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| All Themes | 13.23% | 14.93% | 18.82% | 26.40% | 26.62% |
| Socioeconomic Status | 10.68% | 14.05% | 14.48% | 23.00% | 37.78% |
| Household Characteristics - | 32.5% | 14.6% | 17.1% | 18.8% | 17.1% |
| Racial & Ethnic Minority Status | 0.3% | 6.5% | 16.6% | 34.7% | 41.8% |
| Housing Type & Transportation | 11.93% | 19.88% | 25.22% | 23.70% | 19.27% |
Correlation Plot
Histograms to Check Distributions
Summary Stats
| Name | func_df |
| Number of rows | 3295 |
| Number of columns | 45 |
| _______________________ | |
| Column type frequency: | |
| character | 21 |
| logical | 8 |
| numeric | 16 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| PropName | 0 | 1.00 | 3 | 48 | 0 | 3246 | 0 |
| PropStat | 0 | 1.00 | 6 | 8 | 0 | 2 | 0 |
| MgmtCo | 0 | 1.00 | 4 | 84 | 0 | 469 | 0 |
| OnsiteMgr | 1 | 1.00 | 3 | 26 | 0 | 2248 | 0 |
| Address | 0 | 1.00 | 10 | 46 | 0 | 3288 | 0 |
| City | 0 | 1.00 | 4 | 17 | 0 | 90 | 0 |
| County | 11 | 1.00 | 4 | 9 | 0 | 24 | 0 |
| State | 0 | 1.00 | 2 | 2 | 0 | 1 | 0 |
| Market | 0 | 1.00 | 7 | 7 | 0 | 1 | 0 |
| SubMkt | 0 | 1.00 | 6 | 33 | 0 | 37 | 0 |
| BldgClass | 809 | 0.75 | 1 | 2 | 0 | 9 | 0 |
| BldgTier | 0 | 1.00 | 1 | 3 | 0 | 4 | 0 |
| HsgType | 1 | 1.00 | 7 | 35 | 0 | 15 | 0 |
| BldgType | 0 | 1.00 | 8 | 9 | 0 | 3 | 0 |
| Website | 693 | 0.79 | 16 | 125 | 0 | 2578 | 0 |
| HTypConv | 0 | 1.00 | 5 | 12 | 0 | 2 | 0 |
| ALL_QUIN | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| SOCIO_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| HH_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| RACE_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| HTYPE_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| PetsAllow | 0 | 1.00 | 0.71 | TRU: 2353, FAL: 942 |
| BreedRestr | 942 | 0.71 | 0.87 | TRU: 2041, FAL: 312 |
| WalkArea | 942 | 0.71 | 0.51 | TRU: 1200, FAL: 1153 |
| IntvwReq | 942 | 0.71 | 0.15 | FAL: 1991, TRU: 362 |
| WashArea | 942 | 0.71 | 0.14 | FAL: 2028, TRU: 325 |
| MaxPetsPolicy | 0 | 1.00 | 0.96 | TRU: 3149, FAL: 146 |
| WgtLimitPolicy | 0 | 1.00 | 0.82 | TRU: 2689, FAL: 606 |
| PI | 0 | 1.00 | 0.52 | TRU: 1700, FAL: 1595 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Zip | 0 | 1.00 | 30239.87 | 178.40 | 30002 | 30080 | 30281.00 | 30329.00 | 31907.00 | ▇▁▁▁▁ |
| Units | 0 | 1.00 | 184.43 | 150.26 | 0 | 64 | 164.00 | 276.00 | 1738.00 | ▇▁▁▁▁ |
| Stories | 243 | 0.93 | 3.56 | 3.68 | 1 | 2 | 3.00 | 4.00 | 39.00 | ▇▁▁▁▁ |
| YrBuilt | 68 | 0.98 | 1990.49 | 23.77 | 1840 | 1972 | 1990.00 | 2010.00 | 2025.00 | ▁▁▁▇▇ |
| AvgRent | 739 | 0.78 | 1488.25 | 530.45 | 410 | 1200 | 1400.15 | 1662.51 | 5260.66 | ▇▇▁▁▁ |
| WgtLimit | 942 | 0.71 | 116.61 | 90.11 | 0 | 50 | 75.00 | 255.00 | 255.00 | ▇▆▁▂▆ |
| MaxPets | 942 | 0.71 | 17.68 | 61.06 | 0 | 2 | 2.00 | 2.00 | 255.00 | ▇▁▁▁▁ |
| RefDepMin | 2422 | 0.26 | 305.75 | 120.21 | 10 | 250 | 300.00 | 350.00 | 1000.00 | ▂▇▁▁▁ |
| RefDepMax | 2385 | 0.28 | 355.80 | 188.35 | 25 | 300 | 300.00 | 400.00 | 3300.00 | ▇▁▁▁▁ |
| RefDepAvg | 2376 | 0.28 | 331.17 | 140.12 | 25 | 255 | 300.00 | 400.00 | 1800.00 | ▇▃▁▁▁ |
| NonRefMin | 1327 | 0.60 | 348.28 | 120.77 | 10 | 300 | 350.00 | 400.00 | 2500.00 | ▇▁▁▁▁ |
| NonRefMax | 1325 | 0.60 | 377.42 | 145.59 | 10 | 300 | 350.00 | 450.00 | 2500.00 | ▇▁▁▁▁ |
| NonRefAvg | 1318 | 0.60 | 362.76 | 126.63 | 10 | 300 | 350.00 | 400.00 | 2500.00 | ▇▁▁▁▁ |
| PetRent | 1461 | 0.56 | 23.70 | 16.38 | 8 | 20 | 20.00 | 25.00 | 400.00 | ▇▁▁▁▁ |
| WasteArea | 942 | 0.71 | 1.00 | 0.00 | 1 | 1 | 1.00 | 1.00 | 1.00 | ▁▁▇▁▁ |
| PI_SCORE | 0 | 1.00 | 40.24 | 27.03 | 0 | 0 | 50.00 | 58.00 | 100.00 | ▅▁▇▂▁ |
DFW
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Average Rent vs Pet Inclusivity Score by RPL THEME
All Themes
## `geom_smooth()` using formula = 'y ~ x'Socioeconomic Status
## `geom_smooth()` using formula = 'y ~ x'Household Characteristics
## `geom_smooth()` using formula = 'y ~ x'Race & Ethinc Minorities
## `geom_smooth()` using formula = 'y ~ x'Housing Type & Transportation
## `geom_smooth()` using formula = 'y ~ x'Desnsity Plots of PI SCORE by RPL THEMES
All Themes
Socioeconomic Status
Household Characteristics
Race & Ethinc Minorities
Housing Type & Transportation
Bar Charts of True/False Columns
Average Rent Boxplot by PetsAllow
Percent Frequency of RPL THEMES
| RPL Theme | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| All Themes | 12.00% | 15.85% | 16.13% | 24.98% | 31.05% |
| Socioeconomic Status | 9.0% | 14.8% | 16.3% | 24.5% | 35.3% |
| Household Characteristics - | 32.5% | 13.9% | 15.0% | 18.0% | 20.5% |
| Racial & Ethnic Minority Status | 0.9% | 6.8% | 18.5% | 40.5% | 33.3% |
| Housing Type & Transportation | 11.62% | 18.02% | 22.67% | 24.21% | 23.48% |
Correlation Plot
Histograms to Check Distributions
Summary Stats
| Name | func_df |
| Number of rows | 5060 |
| Number of columns | 45 |
| _______________________ | |
| Column type frequency: | |
| character | 21 |
| logical | 8 |
| numeric | 16 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| PropName | 0 | 1.00 | 3 | 43 | 0 | 4874 | 0 |
| PropStat | 0 | 1.00 | 6 | 8 | 0 | 2 | 0 |
| MgmtCo | 0 | 1.00 | 5 | 88 | 0 | 684 | 0 |
| OnsiteMgr | 1 | 1.00 | 3 | 26 | 0 | 3681 | 0 |
| Address | 0 | 1.00 | 8 | 45 | 0 | 5021 | 0 |
| City | 3 | 1.00 | 4 | 20 | 0 | 142 | 0 |
| County | 22 | 1.00 | 4 | 10 | 0 | 22 | 0 |
| State | 0 | 1.00 | 2 | 2 | 0 | 1 | 0 |
| Market | 0 | 1.00 | 3 | 3 | 0 | 1 | 0 |
| SubMkt | 0 | 1.00 | 5 | 31 | 0 | 72 | 0 |
| BldgClass | 1028 | 0.80 | 1 | 2 | 0 | 9 | 0 |
| BldgTier | 0 | 1.00 | 1 | 3 | 0 | 4 | 0 |
| HsgType | 4 | 1.00 | 7 | 35 | 0 | 14 | 0 |
| BldgType | 0 | 1.00 | 8 | 9 | 0 | 4 | 0 |
| Website | 909 | 0.82 | 18 | 127 | 0 | 4114 | 0 |
| HTypConv | 0 | 1.00 | 5 | 12 | 0 | 2 | 0 |
| ALL_QUIN | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| SOCIO_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| HH_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| RACE_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| HTYPE_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| PetsAllow | 0 | 1.00 | 0.83 | TRU: 4221, FAL: 839 |
| BreedRestr | 839 | 0.83 | 0.82 | TRU: 3465, FAL: 756 |
| WalkArea | 839 | 0.83 | 0.53 | TRU: 2228, FAL: 1993 |
| IntvwReq | 839 | 0.83 | 0.19 | FAL: 3429, TRU: 792 |
| WashArea | 839 | 0.83 | 0.16 | FAL: 3553, TRU: 668 |
| MaxPetsPolicy | 0 | 1.00 | 0.96 | TRU: 4857, FAL: 203 |
| WgtLimitPolicy | 0 | 1.00 | 0.74 | TRU: 3739, FAL: 1321 |
| PI | 0 | 1.00 | 0.62 | TRU: 3120, FAL: 1940 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Zip | 1 | 1.00 | 75476.25 | 1170.77 | 0.00 | 75081.00 | 75226.00 | 76039.00 | 79934.00 | ▁▁▁▁▇ |
| Units | 0 | 1.00 | 200.44 | 185.82 | 0.00 | 72.00 | 186.00 | 288.00 | 7000.00 | ▇▁▁▁▁ |
| Stories | 287 | 0.94 | 3.08 | 2.97 | 1.00 | 2.00 | 2.00 | 3.00 | 49.00 | ▇▁▁▁▁ |
| YrBuilt | 56 | 0.99 | 1991.99 | 22.12 | 1900.00 | 1976.00 | 1986.00 | 2013.00 | 2025.00 | ▁▁▅▇▇ |
| AvgRent | 772 | 0.85 | 1416.98 | 561.70 | 438.89 | 1127.44 | 1328.88 | 1566.49 | 10111.67 | ▇▁▁▁▁ |
| WgtLimit | 839 | 0.83 | 129.90 | 95.09 | 0.00 | 45.00 | 99.00 | 255.00 | 255.00 | ▇▅▁▂▇ |
| MaxPets | 839 | 0.83 | 14.32 | 54.22 | 0.00 | 2.00 | 2.00 | 2.00 | 255.00 | ▇▁▁▁▁ |
| RefDepMin | 2711 | 0.46 | 272.12 | 130.45 | 1.00 | 200.00 | 250.00 | 300.00 | 3010.00 | ▇▁▁▁▁ |
| RefDepMax | 2668 | 0.47 | 296.67 | 161.89 | 20.00 | 200.00 | 250.00 | 350.00 | 3325.00 | ▇▁▁▁▁ |
| RefDepAvg | 2661 | 0.47 | 285.16 | 134.35 | 2.00 | 200.00 | 250.00 | 350.00 | 1825.00 | ▇▂▁▁▁ |
| NonRefMin | 1523 | 0.70 | 319.45 | 118.04 | 15.00 | 250.00 | 300.00 | 400.00 | 1500.00 | ▇▆▁▁▁ |
| NonRefMax | 1507 | 0.70 | 338.63 | 185.49 | 1.00 | 250.00 | 300.00 | 400.00 | 7081.00 | ▇▁▁▁▁ |
| NonRefAvg | 1495 | 0.70 | 328.87 | 138.20 | 15.00 | 250.00 | 300.00 | 400.00 | 3740.50 | ▇▁▁▁▁ |
| PetRent | 1581 | 0.69 | 23.35 | 16.44 | 1.00 | 20.00 | 20.00 | 25.00 | 500.00 | ▇▁▁▁▁ |
| WasteArea | 839 | 0.83 | 1.00 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▇▁▁ |
| PI_SCORE | 0 | 1.00 | 47.51 | 23.48 | 0.00 | 43.00 | 53.00 | 60.00 | 100.00 | ▂▁▇▃▁ |
Los Angeles
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Average Rent vs Pet Inclusivity Score by RPL THEME
All Themes
## `geom_smooth()` using formula = 'y ~ x'Socioeconomic Status
## `geom_smooth()` using formula = 'y ~ x'Household Characteristics
## `geom_smooth()` using formula = 'y ~ x'Race & Ethinc Minorities
## `geom_smooth()` using formula = 'y ~ x'Housing Type & Transportation
## `geom_smooth()` using formula = 'y ~ x'Desnsity Plots of PI SCORE by RPL THEMES
All Themes
Socioeconomic Status
Household Characteristics
Race & Ethinc Minorities
Housing Type & Transportation
Bar Charts of True/False Columns
Average Rent Boxplot by PetsAllow
Percent Frequency of RPL THEMES
| RPL Theme | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| All Themes | 3.3% | 9.9% | 11.5% | 20.0% | 55.2% |
| Socioeconomic Status | 4.8% | 9.8% | 14.3% | 19.7% | 51.5% |
| Household Characteristics - | 18.46% | 13.71% | 17.78% | 25.49% | 24.55% |
| Racial & Ethnic Minority Status | 0.1% | 2.2% | 13.1% | 22.5% | 62.1% |
| Housing Type & Transportation | 1.9% | 8.2% | 16.2% | 24.9% | 48.8% |
Correlation Plot
Histograms to Check Distributions
Summary Stats
| Name | func_df |
| Number of rows | 15321 |
| Number of columns | 45 |
| _______________________ | |
| Column type frequency: | |
| character | 21 |
| logical | 8 |
| numeric | 16 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| PropName | 0 | 1.00 | 2 | 46 | 0 | 14919 | 0 |
| PropStat | 0 | 1.00 | 6 | 8 | 0 | 2 | 0 |
| MgmtCo | 0 | 1.00 | 4 | 84 | 0 | 803 | 0 |
| OnsiteMgr | 1 | 1.00 | 3 | 29 | 0 | 2692 | 0 |
| Address | 0 | 1.00 | 8 | 46 | 0 | 15294 | 0 |
| City | 0 | 1.00 | 4 | 23 | 0 | 203 | 0 |
| County | 50 | 1.00 | 4 | 14 | 0 | 15 | 0 |
| State | 0 | 1.00 | 2 | 2 | 0 | 2 | 0 |
| Market | 0 | 1.00 | 11 | 11 | 0 | 1 | 0 |
| SubMkt | 0 | 1.00 | 7 | 32 | 0 | 31 | 0 |
| BldgClass | 12378 | 0.19 | 1 | 2 | 0 | 9 | 0 |
| BldgTier | 0 | 1.00 | 1 | 3 | 0 | 4 | 0 |
| HsgType | 2 | 1.00 | 7 | 35 | 0 | 15 | 0 |
| BldgType | 0 | 1.00 | 8 | 9 | 0 | 4 | 0 |
| Website | 11289 | 0.26 | 15 | 152 | 0 | 3892 | 0 |
| HTypConv | 0 | 1.00 | 5 | 12 | 0 | 2 | 0 |
| ALL_QUIN | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| SOCIO_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| HH_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| RACE_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| HTYPE_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| PetsAllow | 0 | 1.00 | 0.16 | FAL: 12842, TRU: 2479 |
| BreedRestr | 12842 | 0.16 | 0.77 | TRU: 1911, FAL: 568 |
| WalkArea | 12842 | 0.16 | 0.21 | FAL: 1963, TRU: 516 |
| IntvwReq | 12842 | 0.16 | 0.10 | FAL: 2231, TRU: 248 |
| WashArea | 12842 | 0.16 | 0.07 | FAL: 2309, TRU: 170 |
| MaxPetsPolicy | 0 | 1.00 | 0.97 | TRU: 14825, FAL: 496 |
| WgtLimitPolicy | 0 | 1.00 | 0.94 | TRU: 14352, FAL: 969 |
| PI | 0 | 1.00 | 0.12 | FAL: 13412, TRU: 1909 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Zip | 2 | 1.00 | 90939.76 | 948.21 | 60056 | 90201 | 90723.00 | 91423.00 | 95951 | ▁▁▁▁▇ |
| Units | 0 | 1.00 | 40.39 | 83.82 | 0 | 6 | 12.00 | 40.00 | 4248 | ▇▁▁▁▁ |
| Stories | 1148 | 0.93 | 2.47 | 2.16 | 1 | 2 | 2.00 | 2.00 | 53 | ▇▁▁▁▁ |
| YrBuilt | 315 | 0.98 | 1966.42 | 24.39 | 1866 | 1955 | 1963.00 | 1984.00 | 2025 | ▁▂▆▇▂ |
| AvgRent | 12188 | 0.20 | 2347.84 | 1011.96 | 358 | 1750 | 2206.88 | 2727.65 | 16660 | ▇▁▁▁▁ |
| WgtLimit | 12842 | 0.16 | 132.21 | 106.33 | 0 | 30 | 75.00 | 255.00 | 255 | ▇▁▁▁▇ |
| MaxPets | 12842 | 0.16 | 52.76 | 101.27 | 0 | 2 | 2.00 | 2.00 | 255 | ▇▁▁▁▂ |
| RefDepMin | 13510 | 0.12 | 419.44 | 149.20 | 1 | 300 | 500.00 | 500.00 | 2000 | ▆▇▁▁▁ |
| RefDepMax | 13498 | 0.12 | 437.95 | 157.58 | 10 | 300 | 500.00 | 500.00 | 2000 | ▅▇▁▁▁ |
| RefDepAvg | 13483 | 0.12 | 428.19 | 149.99 | 3 | 300 | 500.00 | 500.00 | 2000 | ▆▇▁▁▁ |
| NonRefMin | 14987 | 0.02 | 291.99 | 175.73 | 20 | 105 | 300.00 | 500.00 | 750 | ▆▇▂▇▁ |
| NonRefMax | 14978 | 0.02 | 333.22 | 212.58 | 20 | 175 | 300.00 | 500.00 | 1000 | ▇▇▇▁▁ |
| NonRefAvg | 14966 | 0.02 | 312.51 | 189.12 | 20 | 150 | 300.00 | 500.00 | 750 | ▇▇▂▇▁ |
| PetRent | 13794 | 0.10 | 53.36 | 46.41 | 2 | 50 | 50.00 | 55.00 | 1300 | ▇▁▁▁▁ |
| WasteArea | 12842 | 0.16 | 1.00 | 0.00 | 1 | 1 | 1.00 | 1.00 | 1 | ▁▁▇▁▁ |
| PI_SCORE | 0 | 1.00 | 9.88 | 23.14 | 0 | 0 | 0.00 | 0.00 | 100 | ▇▁▁▁▁ |
Philidelphia
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Average Rent vs Pet Inclusivity Score by RPL THEME
All Themes
## `geom_smooth()` using formula = 'y ~ x'Socioeconomic Status
## `geom_smooth()` using formula = 'y ~ x'Household Characteristics
## `geom_smooth()` using formula = 'y ~ x'Race & Ethinc Minorities
## `geom_smooth()` using formula = 'y ~ x'Housing Type & Transportation
## `geom_smooth()` using formula = 'y ~ x'Desnsity Plots of PI SCORE by RPL THEMES
All Themes
Socioeconomic Status
Household Characteristics
Race & Ethinc Minorities
Housing Type & Transportation
Bar Charts of True/False Columns
Average Rent Boxplot by PetsAllow
Percent Frequency of RPL THEMES
| RPL Theme | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| All Themes | 16.77% | 17.41% | 14.81% | 20.86% | 30.15% |
| Socioeconomic Status | 17.189% | 18.225% | 17.217% | 20.549% | 26.820% |
| Household Characteristics - | 27.968% | 11.170% | 14.866% | 18.057% | 27.940% |
| Racial & Ethnic Minority Status | 6.1% | 21.6% | 25.6% | 19.0% | 27.7% |
| Housing Type & Transportation | 7.3% | 17.4% | 21.2% | 25.7% | 28.5% |
Correlation Plot
Histograms to Check Distributions
Summary Stats
| Name | func_df |
| Number of rows | 3572 |
| Number of columns | 45 |
| _______________________ | |
| Column type frequency: | |
| character | 21 |
| logical | 8 |
| numeric | 16 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| PropName | 0 | 1.00 | 3 | 47 | 0 | 3512 | 0 |
| PropStat | 0 | 1.00 | 6 | 8 | 0 | 2 | 0 |
| MgmtCo | 0 | 1.00 | 4 | 86 | 0 | 475 | 0 |
| OnsiteMgr | 1 | 1.00 | 3 | 24 | 0 | 1011 | 0 |
| Address | 0 | 1.00 | 9 | 48 | 0 | 3558 | 0 |
| City | 0 | 1.00 | 4 | 20 | 0 | 205 | 0 |
| County | 357 | 0.90 | 5 | 12 | 0 | 10 | 0 |
| State | 0 | 1.00 | 2 | 2 | 0 | 1 | 0 |
| Market | 0 | 1.00 | 12 | 12 | 0 | 1 | 0 |
| SubMkt | 0 | 1.00 | 4 | 33 | 0 | 24 | 0 |
| BldgClass | 2316 | 0.35 | 1 | 2 | 0 | 9 | 0 |
| BldgTier | 0 | 1.00 | 1 | 3 | 0 | 4 | 0 |
| HsgType | 1 | 1.00 | 7 | 35 | 0 | 10 | 0 |
| BldgType | 0 | 1.00 | 8 | 9 | 0 | 4 | 0 |
| Website | 1765 | 0.51 | 17 | 144 | 0 | 1721 | 0 |
| HTypConv | 0 | 1.00 | 5 | 12 | 0 | 2 | 0 |
| ALL_QUIN | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| SOCIO_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| HH_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| RACE_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| HTYPE_QUI | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| PetsAllow | 0 | 1.00 | 0.39 | FAL: 2176, TRU: 1396 |
| BreedRestr | 2176 | 0.39 | 0.79 | TRU: 1099, FAL: 297 |
| WalkArea | 2176 | 0.39 | 0.26 | FAL: 1039, TRU: 357 |
| IntvwReq | 2176 | 0.39 | 0.06 | FAL: 1311, TRU: 85 |
| WashArea | 2176 | 0.39 | 0.08 | FAL: 1279, TRU: 117 |
| MaxPetsPolicy | 0 | 1.00 | 0.93 | TRU: 3318, FAL: 254 |
| WgtLimitPolicy | 0 | 1.00 | 0.81 | TRU: 2894, FAL: 678 |
| PI | 0 | 1.00 | 0.30 | FAL: 2504, TRU: 1068 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Zip | 0 | 1.00 | 19034.15 | 497.99 | 15003 | 19044 | 19123.0 | 19152.25 | 19611 | ▁▁▁▁▇ |
| Units | 0 | 1.00 | 81.90 | 125.82 | 0 | 7 | 34.0 | 102.00 | 1798 | ▇▁▁▁▁ |
| Stories | 192 | 0.95 | 3.83 | 3.65 | 1 | 2 | 3.0 | 4.00 | 46 | ▇▁▁▁▁ |
| YrBuilt | 162 | 0.95 | 1955.04 | 41.74 | 1789 | 1920 | 1962.0 | 1981.00 | 2025 | ▁▁▆▇▆ |
| AvgRent | 2071 | 0.42 | 1632.88 | 626.36 | 450 | 1225 | 1517.5 | 1903.75 | 5907 | ▇▆▁▁▁ |
| WgtLimit | 2176 | 0.39 | 157.91 | 101.19 | 0 | 50 | 175.0 | 255.00 | 255 | ▅▂▁▁▇ |
| MaxPets | 2176 | 0.39 | 48.00 | 97.68 | 0 | 2 | 2.0 | 2.00 | 255 | ▇▁▁▁▂ |
| RefDepMin | 3061 | 0.14 | 293.81 | 125.73 | 2 | 250 | 300.0 | 350.00 | 1310 | ▇▇▁▁▁ |
| RefDepMax | 3059 | 0.14 | 324.32 | 148.25 | 20 | 250 | 300.0 | 350.00 | 1500 | ▇▃▁▁▁ |
| RefDepAvg | 3046 | 0.15 | 307.92 | 128.80 | 20 | 250 | 300.0 | 350.00 | 1310 | ▇▇▁▁▁ |
| NonRefMin | 2698 | 0.24 | 292.75 | 97.04 | 1 | 250 | 300.0 | 350.00 | 700 | ▁▅▇▁▁ |
| NonRefMax | 2767 | 0.23 | 315.07 | 115.06 | 1 | 250 | 300.0 | 350.00 | 1000 | ▁▇▁▁▁ |
| NonRefAvg | 2693 | 0.25 | 301.49 | 101.06 | 1 | 250 | 300.0 | 350.00 | 750 | ▁▇▅▁▁ |
| PetRent | 2592 | 0.27 | 37.61 | 67.32 | 2 | 25 | 35.0 | 40.00 | 2050 | ▇▁▁▁▁ |
| WasteArea | 2176 | 0.39 | 1.00 | 0.00 | 1 | 1 | 1.0 | 1.00 | 1 | ▁▁▇▁▁ |
| PI_SCORE | 0 | 1.00 | 23.55 | 30.58 | 0 | 0 | 0.0 | 54.00 | 100 | ▇▁▃▂▁ |
Info on Variables
PropName = No missing values. Either there is an address listed or the name of the property complex
PropStat = If an apartment complex has 50 units or more, then it is considered active. Otherwise, it is considered inactive. Unclear whether it is available units or total units.
MgmtCo = Assuming that NA values mean there is no management company, 45% of the properties do not have a management company, 55% do have a management company.
OnsiteMgr = 61% of properties do not have an onsite manager, and 39% of the properties have an onsite manager. 4 of the rows were missing a value.
Market = There are 4 Markets. None of the counties overlap with multiple markets. Many counties overlap with multiple submarkets. None of the census tracts overlap with other markets, but they do overlap with some of the submarket. IE a census track has multiple sub markets.
SubMkt = There are a total of 164 SubMarkets and of those, 37 are in Atlanta, 72 are in DFW, 31 are in Los Angeles, and 24 are in Philadelphia. The number of properties in each submarket range from 22 - 307 in Atlanta, 4 - 306 in DFW, 1 - 2150 in Los Angeles, and 17 - 424 in Philadelhpia.
Units = There are some properties that have 0 units, so this most likely means the Units column refers to the number of available units instead of total units. This also means PropStat active vs inactive is in regard to whether there are available units.
BldgClass and BldgTier = Generally refers to the age of the building, location, high quality areas. Class include pluses and minuses while Tier does not. 61% of properties do not have a BldgClass or BldgTier listed.
HsgType = A majority of the housing types are strictly conventional, 83%. The rest contain some combination of section 8, senior, student, and other types. There are only 8 that do not have a housing type listed.
BldgType = A majority of the buildings are Low Rise apartment buildings, 85%. The rest are either Mid Rise, High Rise, or Sky Rise. The taller, the less amount there are. No properties are missing these values.
Stories = Lists the number of stories a building has. If a building has 3 or less stories, then it is a low rise. If it has 4 to 10 stories, then it is a mid rise. If it has 11 - 39 stories, it is a high rise. If it has 40 or more, then it is considered a sky rise apartment.
AvgRent = There are no null values, but over half of the properties list an avg rent of 0, 58%.
PetsAllow = There are no Null values. Of all of the properties, 38% allow pets. There are quite a few properties do not allow pets, but have answers to other questions. For example, some don’t allow pets, but they have breed restrictions. In my opinion, all other columns should be null. A TRUE value for this column results in a score of 28.
BreedRestr = Their are some instances where the property does not allow pets, but they breed restriction field is TRUE. This is most likely an error. For cleaning, any time Pets Allow equaled FALSE, I set all of the other values to FALSE or NA
MaxPets = Some properties, very small amount, allow pets, but their max pets is 0. This value is usually given to Max pets because allow pets is FALSE. A value of 255 means they do not have a max pet limit. There are no instances where PetsAllow = TRUE and their is no value given for Max Pets.
WgtLimit = Similar to max pets, there are instances where the property allows pets, but their weight limit is set to zero. If the property has a value of zero for weight limit, they are given a score of 0. Since there are so few, this shouldn’t affect the analysis, but we do need to figure out how to handle these before the paper. There are no instances where PetsAllow = TRUE and their is no value given for WgtLimit.
Deposit Variables There are 6 variables related to pet deposits, the min, max, and average for refundable and nonrefundable deposits. Originally, properties that allowed pets, but did not require a deposit of any kind were given a value of 0. A pet deposit of 0 does not make sense. If there is not pet rent, then the value should be considered not applicable. When performing numerical operations and statistical analysis with while including zero values will mess with the results. Some properties have both a refundable and nonrefundable pet depoist, and there are some that only have one.
PetRent Petrent had the same situation as the deposit variables, so I performed the same cleaning and logic steps on the PetRent variables as the deposit variables
Pet Inclusive Score Variables I only kept the PI Score variables which is the summation of the other pet inclusive variables.